{talib}: R bindings to TA-Lib

{talib} is an R package for Technical Analysis (TA) and interactive financial charts. The package is a wrapper of TA-Lib and supports over 200 indicators, including candlestick patterns.

Data format

All functions in {talib} expect the input x to be coercible to data.frame with columns named according to OHLC-V conventions. The package ships with several built-in datasets:

str(talib::BTC)
#> 'data.frame':    366 obs. of  5 variables:
#>  $ open  : num  42274 44185 44966 42863 44191 ...
#>  $ high  : num  44200 45918 45521 44799 44392 ...
#>  $ low   : num  42181 44152 40555 42651 42362 ...
#>  $ close : num  44185 44966 42863 44191 44179 ...
#>  $ volume: num  831 2076 2225 1791 2483 ...

Column names are case-sensitive

Column names must be lowercase: open, high, low, close, and volume. Names such as Close, CLOSE, or Adj.Close will not be recognized. If your data uses different names you have two options: rename the columns, or remap them with the cols argument (see Column selection with cols).

## rename columns to uppercase;
## this will fail
x <- talib::BTC
colnames(x) <- c("Open", "High", "Low", "Close", "Volume")

talib::RSI(x)
#> Error in `relative_strength_index.default()`:
#> ! Expected to find columns 'close' similar columns found: 'Close'

Not every column is always required

Different indicators use different subsets of the OHLC-V columns. The table below gives a rough guide:

Indicator type	Default columns	Example
Univariate (most)	close	RSI(), SMA(), EMA()
High-Low based	high + low	SAR(), AROON()
High-Low-Close	high + low + close	STOCH(), CCI(), ATR()
Full OHLC	open + high + low + close	All candlestick patterns
Volume-based	volume (+ price columns)	OBV(), AD(), MFI()

A data.frame that only contains high, low, and close is perfectly valid input for STOCH() or CCI()—columns that are not needed are simply ignored.

Computing indicators

Basic usage

Pass an OHLC-V object directly to any indicator function:

tail(
    talib::bollinger_bands(talib::BTC)
)
#>                     UpperBand MiddleBand LowerBand
#> 2024-12-26 01:00:00 104478.35   98217.88  91957.42
#> 2024-12-27 01:00:00 100877.73   97020.16  93162.59
#> 2024-12-28 01:00:00  99886.22   96516.01  93145.81
#> 2024-12-29 01:00:00  99871.12   96134.41  92397.71
#> 2024-12-30 01:00:00  99713.92   95620.42  91526.92
#> 2024-12-31 01:00:00  99373.89   95236.42  91098.95

All indicator functions follow the same S3 dispatch pattern, with methods for data.frame, matrix, numeric, and plotly. The return type matches the input type:

## data.frame in -> data.frame out
class(
    talib::RSI(talib::BTC)
)
#> [1] "data.frame"

## matrix in -> matrix out
class(
    talib::RSI(talib::SPY)
)
#> [1] "matrix" "array"

## numeric vector in -> numeric vector out
is.double(
    talib::RSI(talib::BTC$close)
)
#> [1] TRUE

Function naming

Every indicator has a descriptive snake_case name and an uppercase alias that mirrors the TA-Lib C function name. Both are interchangeable:

## these are equivalent
identical(
    talib::relative_strength_index(talib::BTC, n = 14),
    talib::RSI(talib::BTC, n = 14)
)
#> [1] TRUE

Lookback and NA values

Most indicators require a minimum number of observations before they can produce a value. This is called the lookback period. The first lookback rows of the result will be NA:

## SMA with n = 5 has a lookback of 4
head(
    talib::SMA(talib::BTC, n = 5),
    n = 7
)
#>                          SMA
#> 2024-01-01 01:00:00       NA
#> 2024-01-02 01:00:00       NA
#> 2024-01-03 01:00:00       NA
#> 2024-01-04 01:00:00       NA
#> 2024-01-05 01:00:00 44076.95
#> 2024-01-06 01:00:00 44038.77
#> 2024-01-07 01:00:00 43835.33

The lookback is stored as an attribute on the result:

x <- talib::SMA(talib::BTC, n = 20)
attr(x, "lookback")
#> [1] 19

Column selection with cols

The cols argument accepts a one-sided formula (~) that selects which columns to use for the calculation. Every indicator has a sensible default, but cols lets you override it.

Univariate indicators

For indicators that operate on a single series (e.g., RSI, SMA), the default is ~close. Pass a different column to calculate the indicator on that series instead:

## RSI on 'high' instead of 'close'
tail(
    talib::RSI(talib::BTC, cols = ~high)
)
#>                          RSI
#> 2024-12-26 01:00:00 48.47864
#> 2024-12-27 01:00:00 41.75995
#> 2024-12-28 01:00:00 37.52957
#> 2024-12-29 01:00:00 36.63920
#> 2024-12-30 01:00:00 36.02487
#> 2024-12-31 01:00:00 41.25189

Multivariate indicators

For indicators that require multiple columns, cols remaps which columns are used. The order in the formula must match the order expected by the indicator:

## Stochastic defaults to ~high + low + close;
## here we swap 'close' for 'open'
tail(
    talib::STOCH(
        talib::BTC,
        cols = ~high + low + open
    )
)
#>                        SlowK    SlowD
#> 2024-12-26 01:00:00 53.15136 55.31111
#> 2024-12-27 01:00:00 50.02536 53.85337
#> 2024-12-28 01:00:00 43.95281 51.73485
#> 2024-12-29 01:00:00 43.59808 49.54381
#> 2024-12-30 01:00:00 42.99018 47.87554
#> 2024-12-31 01:00:00 41.67922 46.56459

Extra arguments via ...

Additional arguments are forwarded to model.frame(). This is useful for computing an indicator on a subset of the data:

## Bollinger Bands on the first 100 rows only
tail(
    talib::BBANDS(
        talib::BTC,
        subset = 1:nrow(talib::BTC) %in% 1:100
    )
)
#>                     UpperBand MiddleBand LowerBand
#> 2024-04-04 02:00:00  72704.62   69067.64  65430.67
#> 2024-04-05 02:00:00  72502.38   68854.97  65207.56
#> 2024-04-06 02:00:00  72430.18   68802.22  65174.26
#> 2024-04-07 02:00:00  72066.18   68658.42  65250.65
#> 2024-04-08 02:00:00  72626.19   68831.42  65036.64
#> 2024-04-09 02:00:00  72549.39   68783.24  65017.08

Handling missing values with na.ignore

Real-world data often contains missing values. By default (na.ignore = FALSE), any NA in the input propagates through the entire calculation, which can fill the result with NAs.

Setting na.ignore = TRUE strips NA rows before calculation, computes the indicator on the clean data, and then re-inserts NAs at their original positions:

## inject some NAs
x <- talib::BTC
x$close[c(10, 50, 100)] <- NA

## default: NAs propagate
sum(is.na(
    talib::RSI(x)
))
#> [1] 366

## na.ignore = TRUE: NAs are skipped
sum(is.na(
    talib::RSI(x, na.ignore = TRUE)
))
#> [1] 13

The stripped rows are restored in the output, so the result always has the same number of rows as the input:

nrow(talib::RSI(x, na.ignore = TRUE)) == nrow(x)
#> [1] TRUE

Moving Average specifications

Several indicators accept a Moving Average specification for their smoothing component. MA functions such as SMA(), EMA(), WMA(), DEMA(), TEMA(), TRIMA(), KAMA(), and T3() serve a dual purpose:

• With x: compute the Moving Average on the data.
• Without x: return a specification (a named list) that other indicators use internally.

## SMA as a specification
str(
    talib::SMA(n = 20)
)
#> List of 2
#>  $ n     : int 20
#>  $ maType: int 0

This specification can be passed to indicators like bollinger_bands() or stochastic() to control the type of smoothing:

## Bollinger Bands with an EMA(20) middle band
tail(
    talib::bollinger_bands(
        talib::BTC,
        ma = talib::EMA(n = 20)
    )
)
#>                     UpperBand MiddleBand LowerBand
#> 2024-12-26 01:00:00  104645.9   98186.94  91727.99
#> 2024-12-27 01:00:00  104677.9   97804.16  90930.38
#> 2024-12-28 01:00:00  104597.2   97548.60  90500.04
#> 2024-12-29 01:00:00  104577.9   97169.11  89760.28
#> 2024-12-30 01:00:00  104576.0   96736.62  88897.26
#> 2024-12-31 01:00:00  104478.2   96417.96  88357.68

## Stochastic with WMA smoothing
tail(
    talib::stochastic(
        talib::BTC,
        slowk = talib::WMA(n = 5),
        slowd = talib::EMA(n = 3)
    )
)
#>                        SlowK    SlowD
#> 2024-12-26 01:00:00 62.93474 57.17934
#> 2024-12-27 01:00:00 52.56693 54.87313
#> 2024-12-28 01:00:00 43.17803 49.02558
#> 2024-12-29 01:00:00 27.93503 38.48031
#> 2024-12-30 01:00:00 19.49793 28.98912
#> 2024-12-31 01:00:00 22.83957 25.91435

Candlestick pattern recognition

Candlestick pattern functions return an integer matrix: 1 for bullish, -1 for bearish, and 0 for no pattern. The encoding can be changed via options(talib.normalize = FALSE) to use 100/-100 instead.

x <- talib::harami(talib::BTC)
tail(x)
#>                     CDLHARAMI
#> 2024-12-26 01:00:00         0
#> 2024-12-27 01:00:00         0
#> 2024-12-28 01:00:00         0
#> 2024-12-29 01:00:00         0
#> 2024-12-30 01:00:00         0
#> 2024-12-31 01:00:00         0

## find all bullish occurrences
talib::BTC[which(x == 1), ]
#>                         open     high      low    close    volume
#> 2024-01-13 01:00:00 42770.74 43244.75 42417.54 42842.43 1389.8450
#> 2024-01-19 01:00:00 41286.00 42144.29 40250.00 41622.00 2204.1180
#> 2024-01-23 01:00:00 39521.03 40135.90 38508.01 39871.07 1691.9766
#> 2024-03-06 01:00:00 63803.50 67655.97 62834.01 66112.32 2152.6899
#> 2024-04-03 02:00:00 65477.34 66944.00 64500.00 65986.02  730.2592
#> 2024-04-16 02:00:00 63449.24 64392.44 61641.34 63820.00  975.8047
#> 2024-05-02 02:00:00 58267.89 59638.02 56893.92 59066.67 1608.7340
#> 2024-06-27 02:00:00 60828.18 62346.00 60559.38 61629.99 1343.3199
#> 2024-12-20 01:00:00 97385.63 98138.88 92129.00 97767.96 6001.8659

Note: For a detailed treatment of candlestick lookback and sensitivity parameters, see vignette("candlestick", package = "talib").

Charting

The package includes interactive charting built on {plotly}. The two main functions are chart() and indicator(), which work like plot() and lines():

{
    ## main candlestick chart
    talib::chart(talib::BTC)

    ## add Bollinger Bands
    talib::indicator(talib::bollinger_bands)

    ## add identified Harami patterns
    talib::indicator(talib::harami)

    ## add MACD as a sub-chart
    talib::indicator(talib::MACD)
}

indicator() automatically determines whether a given indicator belongs on the main price chart (e.g., Bollinger Bands, SAR) or as a sub-chart (e.g., RSI, MACD).

Note: For themes, chart options, and advanced charting, see vignette("charting", package = "talib").

Contributing and bug reports

The underlying library, TA-Lib, is a long-standing and well-known library but this R wrapper is still in its early stage. All contributions, suggestions, and bug reports are welcome.